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Neural representation of abstract task structure during generalization

View ORCID ProfileAvinash R. Vaidya, Henry M. Jones, Johanny Castillo, David Badre
doi: https://doi.org/10.1101/2020.07.21.213009
Avinash R. Vaidya
1Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
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  • For correspondence: avinash_vaidya@brown.edu
Henry M. Jones
1Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
2Department of Psychology, Stanford University
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Johanny Castillo
1Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
3Department of Psychology and Brain Sciences, University of Massachusetts Amherst
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David Badre
1Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
4Carney Institute for Brain Science, Brown University
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Abstract

Abstract task representations enable generalization, including inferring new behaviors based on prior knowledge without additional training. However, evidence for a neural representation that meets this benchmark is surprisingly limited. Here, using functional MRI (fMRI), we observed that abstract task structure was represented within frontoparietal networks during generalization. These results reveal the neural systems supporting a vital feature of human cognition: the abstraction of task knowledge to infer novel behaviors.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted July 21, 2020.
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Neural representation of abstract task structure during generalization
Avinash R. Vaidya, Henry M. Jones, Johanny Castillo, David Badre
bioRxiv 2020.07.21.213009; doi: https://doi.org/10.1101/2020.07.21.213009
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Neural representation of abstract task structure during generalization
Avinash R. Vaidya, Henry M. Jones, Johanny Castillo, David Badre
bioRxiv 2020.07.21.213009; doi: https://doi.org/10.1101/2020.07.21.213009

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